Educational Technology Platforms and Shift in Pedagogical Approach to Support Computing Integration Into Two Sophomore Civil and Environmental Engineering Courses

Computing is one of the major focal skill sets, within the scope of a department-wide curriculum modernization, at the sophomore level for civil and environmental engineering (CEE) students. Two courses were chosen to lead the modernization effort, with a view to scaffolding to upper level courses. The first course is on system engineering and economics (SEE). The second course is an introduction to engineering risk and uncertainty (ERU) through introductory probability and statistics. Python was chosen as the computational tool for SEE and R was chosen for ERU. Two tools were chosen to communicate that 1) computing literacy is more than the specific tool used; 2) different tools are better suited for different applications; 3) different tools can work together, taking advantage each tool’s strengths for a given application. Teaching of the computing language was integrated within the substantive material for each course, after a set of practice assignments, in the first 2-3 weeks of instruction. To support computing practice, we took advantage of an on-line educational platform which allows, coding questions, problem randomization and emphasizes mastery. The system supports homework assignments, class worksheets and frequent exams. To allow space for student active learning, both courses switched to a student-centered learning mode. Students watch one or two lecture video quizzes, with 5-12 min duration, before they go to class. During class, students work on worksheets which aim to student working with the material for a deeper level of understanding, sometimes by questions pushing the students to go beyond the theory narration in the video and sometimes by applying the concepts to solve problems with CEE context. Students are assigned to teams of 3-5. Because of precautions for the spread of COVID-19, all student teams connect on Zoom and teams are pre-assigned to breakout out rooms, where teams cooperate, while the instructor and TAs visit the breakout rooms to answer questions. We are currently, in the first semester of implementing the student-centered learning model. The goal of the paper is to present our technology enabled approach along with initial assessment of outcomes. Quantitative and qualitative assessment of this first implementation of computing with a student-centered learning model will be based on the following sets of available information: 1) Grading of submitted work; 2) Student feedback and reflection of their learning experience; and 3) Informal assessment from instructor interaction with the student teams. The focal points of our assessment are, respectively: 1) student learning as demonstrated through formative and summative assignment evaluation, 2) student perceptions of the student-centered model and 3) degree of student engagement during class and retention of the class community spirit, despite the online environment.